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Review

Statistical Tools Application for Literature Review: A Case on Maintenance Management Decision-Making in the Steel Industry

by
Nuno Miguel de Matos Torre
1,*,
Valerio Antonio Pamplona Salomon
1 and
Luis Ernesto Quezada
2
1
Department of Production, Universidade Estadual Paulista, Av. Ariberto P. Cunha 333, Guaratingueta 12516-410, SP, Brazil
2
Department of Industrial Engineering, Universidad de Santiago de Chile, Av. Víctor Jara 3769, Santiago 9170020, Chile
*
Author to whom correspondence should be addressed.
Stats 2025, 8(3), 80; https://doi.org/10.3390/stats8030080
Submission received: 25 July 2025 / Revised: 5 September 2025 / Accepted: 7 September 2025 / Published: 12 September 2025
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Abstract

Literature review plays a crucial role in research. This paper explores bibliometrics, which utilize statistical tools to evaluate the researcher’s scientific contributions. Its intent is to map frequently cited articles and authors, identify top sources, track publication years, explore keywords and their co-occurrences, and show article distribution by thematic area and country. Additionally, it provides a thematic map of relevance and progress, with special attention to interdisciplinary work. Finally, it also makes use of research findings in maintenance management decision-making, where the findings reveal that the literature provides valuable insights into the impact of the Analytic Hierarchy Process (AHP) method. Despite advancements in maintenance management, gaps persist in comprehensively addressing core themes, evolutionary trends, and future research directions. This research aims to bridge this gap by providing a detailed examination of the application of bibliometric analysis employing statistical tools to measure researchers’ scientific contributions, concerning the AHP method applications in maintenance management within the steel industry. The study confirmed that tools like VOSviewer and the Bibliometrix package in R can extract relevant information regarding bibliometric laws, helping us understand research patterns. These findings support strategic decision-making and the evaluation of scientific policies for researchers and institutions.

1. Introduction

A literature review is essential for obtaining a thorough understanding of a research problem by exploring several viewpoints and elements of existing knowledge [1]. Contrary to the excessive search for information on websites that provide search engines or generative artificial intelligence, this work highlights the importance of scientific database reviews such as Scopus and Web of Science.
According to [2], a literature review includes numerous steps, including searching for data and analyzing numerous published materials based on their central issues, first, from an evaluation in their abstracts, and, secondly, from the proof presented. The following step is reviewing the findings and coverage suggestions already available to learn about the state of knowledge in the area under study.
A bibliographic review, regardless of its nature, is a critical discussion that analyzes various publications, whether conceptual, theoretical, statistical, or ideological. This review is a key component of broader scientific writing. Part of this process includes examining and analyzing the chosen academic articles’ conclusions using a statistically based procedure [3].
According to [4], bibliometric analysis of research provides insights into publications’ key contributors, trends, and methodological developments, and relies on mathematics and statistics, providing intuitive information for further discussions [5].
Bibliometric analysis can identify research fields in maintenance management, utilizing mathematical graphs, known as networks, to effectively represent interconnected literature [6].
Maintenance management should be supported by effective strategies and methods to clearly define its policies. This is essential for ensuring competitiveness and profitability across all industry sectors. The ongoing focus on productivity and reducing waste as a competitive advantage has increased the significance of maintenance management within organizations [7].
Maintenance management plays a crucial role in various industries, particularly in heavy industries, and is influenced by several factors. Multi-criteria decision-making (MCDM) can assist in identifying optimal solutions. According to the current literature, the AHP is one of the most widely used methods for addressing this issue [8].
The AHP method is an effective tool for assessing sustainability within steel plants. Regarding production efficiency in this sector, costs have a direct influence on manufacturing setup, profitability, and long-term sustainability. Poor maintenance management can lead to capital losses and inefficient use of human resources in these organizations [9].
The AHP is a decision-making method that comprises a structured framework with decision problems containing objectives, criteria, sub-criteria, and alternative solutions. It aims to help decision-makers understand the problems and make assertive decisions [10].
Effective decision-making is crucial for maintaining the industry’s operations in the steel industry. This research aims to provide a detailed examination of the application of bibliometric analysis employing statistical tools to measure researchers’ scientific contributions, concerning the AHP method applications in maintenance management within the steel industry.
The steel industry generates 40 million jobs worldwide and plays a vital role in a country’s development; however, these enterprises have a high degree of technological complexity. In this context, it is necessary to foster a continuous improvement methodology in the processes and product development [11].
We selected the AHP method because it offers a thorough and logical approach to modeling decision problems. This method effectively represents and quantifies the variables involved, ensuring that all participants understand the process clearly. Additionally, as the most widely used MCDM method, AHP can be applied in operational settings without the need for specialized software acquisition by the organization.
The guiding research question for this study is as follows: How can statistical tools be helpful in the research panorama of a bibliometric analysis regarding the application of the AHP method in maintenance management at the steel industry? To answer this question, we consider three bibliometric laws: Lotka’s Law, Zipf’s Law, and Bradford’s Law. These laws help us understand the research patterns relevant to our objective. Additionally, we employ VOSviewer (version 1.6.20) and the Bibliometrix R (version 4.3.3) package in R, which has functions for extracting information pertinent to the three bibliometric laws.
The significance of bibliometric laws—Lotka’s Law, Zipf’s Law, and Bradford’s Law—is crucial for understanding patterns in the production of scientific knowledge. When applied in bibliometric methodology, these laws offer valuable insights into academic output, allowing for a deeper analysis of the dynamics of scientific research [12].

2. Theoretical Background

2.1. Bibliometrics and Statistical Tools

Bibliometrics uses statistical indicators to measure the quantity and characteristics of scientific publications. This field encompasses collecting, processing, and managing bibliographic data related to these publications. Additionally, bibliometrics can provide insights into the impact of research, identify key institutions contributing to scientific work, and explore the relationships between different publications and related topics from both quantitative and qualitative perspectives [13].
According to [14], the bibliometric statistical indicators emerged in the 1960s and 1970s and have become widely used due to the expansion of relevant databases. Furthermore, these indicators offer valuable measures of the results of scientific research activity and performance and have become standard tools for evaluating research [15].
Bibliometrics refers to the mathematical and statistical methods applied to evaluate the development of technological knowledge and approaches in any research area. This evaluation includes assessing the overall performance of contributions on specific topics and analyzing the interactions among researchers and their areas of study. In other words, bibliometrics involves examining numerical indicators and the interactions identified within the frame of scientific data [4,5].

2.2. VOSviewer and Bibliometrix

VOSviewer is a powerful tool for displaying large bibliometric maps in a way that is easy to understand. It can create maps of authors or journals using cocitation data, as well as maps of keywords based on co-occurrence data. The software includes features for zooming, scrolling, and searching, which make it easier to examine a map in detail. VOSviewer is particularly effective for maps that include a moderate to large number of items (for example, at least 100 items). Many other programs used for bibliometric mapping do not present maps as effectively [16].
VOSviewer is a bibliometric analysis tool using network analysis, text mining, and grouping techniques for three visualizations: network, overlap, and density visualization [17].
According to [18], Bibliometrix is an open-source tool designed for conducting comprehensive science mapping analyses of scientific literature. Developed in R, it offers flexibility and facilitates integration with other statistical and graphical packages. As bibliometrics is an ever-evolving field, Bibliometrix can be quickly updated and adapted to new developments. Its growth is supported by a large and active community of developers, including prominent researchers.
Key bibliometric concepts like Bradford’s, Lotka’s, and Zipf’s laws form the foundation of the Bibliometrix tools in R. Bradford’s law measures journal productivity, Lotka’s law evaluates author productivity, and Zipf’s law examines word frequency in papers. Researchers can apply these laws to uncover patterns and insights [19].
In this context, we used the three classic laws of bibliometrics to analyze the results: (1) Lotka’s Law for evaluating author productivity [20], (2) Bradford’s Law to assess journal dissemination [21], and (3) Zipf’s Law to examine keyword frequency [22].

2.3. Three Classic Laws of Bibliometrics

According to [23], Bradford’s Law suggests that as articles are created on a new subject, they tend to be submitted to a limited number of relevant journals. If these articles are accepted, those journals will attract additional articles as the subject area develops.
Bradford’s Law is a significant concept in bibliometrics that assists academic libraries with literature searches and acquisitions. It helps model the development of scientific knowledge and evaluate journal coverage for specific subjects [24,25]. The limitations include its dependence on specific data distribution patterns that are often absent in real-world datasets. Furthermore, when this pattern is disrupted, there is a risk of incorrectly identifying core journals [25].
Bradford’s Law states that if journals are arranged in descending order of productivity and divided into p groups with an equal number of papers, the number of journals in each group, denoted as ni, follows a specific ratio (n1: n2:…:np = 1:k:…: kp-1), where k is the Bradford multiplier [24].
As per [26], Lotka’s Law pertains to the productivity of authors and relies on the idea that some researchers publish a considerable amount while others publish very little. This law describes a consistent relationship between the number of core authors who produce multiple articles and the number of occasional authors who publish only one article. It also illustrates an inverse relationship between the total number of published articles and the number of core authors involved [27].
This law has is valuable in bibliometric studies as it predicts an author’s contribution to publications and provides insights into scientific productivity and the relationship between authors and their articles. Its limitations are that it does not perfectly fit all disciplines or publication contexts. Additionally, its accuracy depends on the quality and completeness of the data used. It may also be influenced by issues such as incomplete databases or variations in how publications are indexed [28,29].
The author of [27] highlighted that the relationship between the number of authors and the number of articles published within any scientific field follows the inverse square law 1/n2. In other words, in a given period, when analyzing a certain number of articles, the number of authors who wrote two articles would equal 1/4 of those who wrote one. Similarly, the number of authors who wrote three articles would equal 1/9 of those who wrote one, and so on.
As mentioned by [30], Zipf’s Law quantifies the frequency of term occurrences in the literature, assisting in compiling a list of the most frequently used terms in a specific research domain. The authors of [31] mention that the benefits of this law consist in providing a clear way to understand rank-frequency distributions and help illuminate the formation of hierarchies across various systems. It can be applied widely, from linguistic corpora to urban structures. However, it has some limitations, particularly its focus on distribution tails, which can sometimes lead to the neglect of significant data that may be relevant.
Zipf’s Law describes the relationship between the frequencies of words in large collections of texts. When the words (w) are arranged according to their relative frequencies (fw), from highest to lowest, their frequencies are approximately inversely proportional to their ranks (rw). This relationship can be expressed as f(rw) ≈ A/rw, where A is a constant that depends on the total number of words in the analyzed text. The probability distribution p(wr) = A/rw is referred to as Zipf’s distribution [32].

2.4. AHP and Maintenance

The AHP method, developed by [33,34,35], is one of the most widely applied MCDM methods for decision-making across various fields, including engineering, computer science, mathematics, logistics, health, industry, and education [36].
The most effective approach to solving multi-criteria problems is to use MCDM methods in operations research. The AHP is a leading MCDM technique that has been applied to address several complex MCDM challenges [37].
According to the AHP method, organizing a problem begins with defining an overall objective. The problem is outlined, identifying criteria, sub-criteria, and alter-natives, thus creating a hierarchical structure. Experts compare criteria, sub-criteria, and alternatives according to their relative importance, called priority in AHP [38].
As highlighted by [36], AHP has been used effectively in a wide range of fields, including product design, investment, education, logistics, finance, policy-making, engineering, and industry. This methodology helps establish the priorities and preferences of groups or individuals during the decision-making process.
One of the many MCDM techniques is the AHP method, which was created by [30]. The AHP method is particularly valuable for its ease of use and effectiveness in aiding decision-making. It remains the most widely utilized decision-making method across all fields of scientific and business knowledge [39].
According to [40], a clear understanding of the sustainability assessment of the AHP method, coupled with a BOCR analysis, can provide a clear understanding of the interdependencies of the benefits (B), opportunities (O), costs (C), and risks (R) of projects in terms of exploiting the B and O and avoiding the C and R.
Maintenance is defined as the combination of all administrative, managerial and technical actions needed throughout the life cycle of an item to retain or restore it to a state where it can effectively perform its required function [41].
According to [42], planning maintenance activities are related to keeping the system operating to the desired standards. Maintenance coordination is fundamental to guaranteeing the life cycle of any asset, where availability plays an essential role in organizations as referred by [43,44,45].
The maintenance process has significant potential for promoting sustainable manufacturing processes and has become an essential business function. Maintenance affects the technical conditions of assets and equipment, such as reliability and availability. Additionally, maintenance addresses sustainability concerns, including environmental impact, safety performance, and the consumption of energy and resources [46].
As referred by [47], steel plants foster a culture of continuous improvement in their operational processes, particularly in product development and research activities, which is essential. In this context, once it involves a comprehensive system for managing the lifespan of assets, maintenance is essential to the operational process, as it encompasses both technical and administrative activities. In organizations, effective maintenance planning plays a crucial role in reducing operating costs. Each maintenance strategy has strengths and weaknesses, and managers must make decisions based on the level of criticality [48].
Maintenance includes several activities, from simple corrective interventions to comprehensive asset management. Its goal is to enhance functionality, availability, quality, and cost-efficiency, making it a strategic focus for organizations [49].
There are four primary maintenance strategies: autonomous, corrective, preventive, and predictive [50]. To avoid production losses, steel companies often implement preventive maintenance, which aims to replace equipment before its useful life is exhausted [51]. Predictive maintenance, which focuses on monitoring the condition of equipment, has emerged as a promising solution for manufacturing industries [52]. Additionally, the industry is increasingly considering autonomous maintenance due to its potential to automate operational procedures [53].
Several authors have implemented the AHP in case studies to establish and validate weights, like [54] for predictive maintenance decision-making, Ref. [55] to rank the performance indicators in the context of IoT-enabled DG monitoring, and [56] to integrate AHP with machine learning (ML) to reduce the number of criteria and simplify the decision-making process.
The AHP is applied in both the steel and chemical/power industries, though for different purposes. In the steel industry, AHP is commonly used for material selection [57], process optimization [58,59], and maintenance [49]. In contrast, the chemical and power industries frequently utilize AHP for sustainability [60], safety [61], energy efficiency [62], and strategic planning [63].

3. Research Methodology

This research selected articles that describe the use of decision-making methods (AHP) with a view to applying the concepts to maintenance management in the steel industry. Five stages have been carried out to achieve this end, as shown in Figure 1.
The first stage involved selecting the appropriate database for the bibliometric analysis. Currently, the two most significant databases are Clarivate’s Web of Science and Elsevier’s Scopus [64]. We chose the Scopus database.
The second stage involved a manuscript selection step. Key search terms were selected from 2015 to 2024. This study focused on the terms in the title, abstract, and keywords (TITLE-ABS-KEY). The documents were identified and manually entered into spreadsheets.
The third stage involved search software using the VOSViewer tool and Bibliometrix. VOSViewer is utilized to group publications and analyze the resulting groupings in the literature [65,66], while Bibliometrix provides a set of tools for quantitative analysis to help researchers carry out bibliometric analysis [67].
The fourth stage involved reading the documents selected in Stage 2 and interpreting the groupings identified in Stage 3. According to [68], bibliometric analysis is a rigorous and scientific method for gaining a comprehensive understanding of any field of study [66]. This document employs a bibliometric analysis approach to examine and draw conclusions from articles sourced from the Scopus database. The key bibliometric laws referenced include Bradford’s Law, which pertains to journal productivity; Lotka’s Law, which relates to the scientific output of authors; and Zipf’s Law, which addresses word frequency [26].
For this analysis, in addition to the VOSViewer tool, we used the Bibliometrix package of the R program, which has functions for extracting information pertinent to the three laws of bibliometrics. Bibliometrics offers historiography a chronological network plot that generates a grid distribution of citations over time and creates graphs based on local and global citation scores [18].
The fifth stage involved verifying the results obtained. By using statistical tools, we analyzed the scientific contributions related to the theme of this research. We found that the proposed topic remains relevant in the scientific field, offering numerous possibilities for further study. Additionally, we gathered data on connections, patterns, and trends concerning the application of the AHP method in maintenance management within the steel industry. This research is classified according to [69].
The objectives of this study are twofold. First, it aims to provide a descriptive overview, focusing on the temporal evolution of publications, the number of authors, institutions, and countries contributing to the literature. Second, it is exploratory, aiming to analyze data and explore the associations between concepts identified in existing research. This study adopts a qualitative inquiry approach and is classified as a bibliometric analysis, as it utilizes statistical tools to measure researchers’ scientific contributions.

4. Results

4.1. Data Collection

By combining the Boolean operators “OR” with the words “AHP” OR “Analytic Hierarchy Process” we have a total of 41,938 for Scopus and 26,485 for Web of Science. Concerning “Maintenance” we have a total of 404,285 for Scopus and 274,674 for Web of Science, and by combining the Boolean operators “OR” with the words “Steel industry” OR “Steel Plant” we have a total of 10,136 for Scopus and 4679 for Web of Science, as showing in Table 1.
We adopted the “TITLE-ABS-KEY” selection method, with a one-year period between them. The inclusion criteria include articles published between 2015 and 2024.
The database contains a total of 762,197 scientific articles, with 456,359 relevant to Scopus and 305,838 relevant to Web of Science. This clearly shows that Scopus is more comprehensive than the Web of Science database.
Scopus was chosen as the research database due to its extensive coverage of peer-reviewed literature across various fields, including those relevant to the main focus of this research. Its advanced search and analysis features, such as author matching and the ability to track research output over time, simplify the process of finding relevant articles and analyzing research trends. Additionally, Scopus provides a user-friendly interface and a wide range of resources, including open-access articles. According to [70], the Scopus database comprises over 1.7 billion citations, offering more comprehensive bibliographic data coverage compared to the Web of Science.
Key search terms through the combinations of Boolean operators (“AND”; “OR”) with the words “AHP” OR “Analytic Hierarchy Process”, “Maintenance”, “Steel Industry” OR “Steel Plant” have been selected from the Scopus database in the search field TITLE-ABS-KEY. The dataset files are available https://hdl.handle.net/11449/313301.
The advanced query in the Scopus database has been conducted as follows:
(TITLE-ABS-KEY (maintenance) AND TITLE-ABS-KEY (ahp) OR TITLE-ABS-KEY (“analytic hierarchy process”)) AND PUBYEAR > 2014 AND PUBYEAR < 2025, obtaining 1550 records.
(TITLE-ABS-KEY (“Steel industry”) OR TITLE-ABS-KEY (“Steel plant”) AND TITLE-ABS-KEY (maintenance)) AND PUBYEAR > 2014 AND PUBYEAR < 2025, obtaining 297 records.
(TITLE-ABS-KEY (“Steel industry”) OR TITLE-ABS-KEY (“Steel plant”) AND TITLE-ABS-KEY (ahp) OR TITLE-ABS-KEY (“analytic hierarchy process”)) AND PUBYEAR > 2014 AND PUBYEAR < 2025, obtaining 60 records.
Duplicate articles between the Web of Science and Scopus databases were not excluded from the initial analyses presented in Table 2 and Table 3, as this would provide a clearer overview of the data collected.

4.2. Data Analysis

The scientific publications with the most citations per keyword are displayed in Table 3. In addition to the bibliographic reference containing the author, the name of the article and the year of publication are also shown.
Figure 2 illustrates the subject areas, showing that engineering dominates with 30% of the documents. The other areas of knowledge are classified as Computer Science, Environmental Science, Energy, Business, Management and Accounting, Mathematics, Social Sciences, Materials Science, Decision Sciences, Physics and Astronomy, Earth and Planetary Sciences, Agricultural and Biological Sciences, Chemical Engineering and Medicine.
The top ten distribution of authors by country is shown in Figure 3, where China stands out with 643 published articles, followed by India (224), the United States (125), and Indonesia (111). The other countries listed appear with fewer than 100 articles.
Figure 4 illustrates the contemporaneity, showing that most articles have been published recently, indicating an upward trend over the years.
The leading sources are selected from the sources section of R Bibliometrix. Using Bradford’s Law, we identified 1007 sources that had published documents on AHP OR “Analytic Hierarchy Process”, AND Maintenance, AND “Steel Industry” OR “Steel Plant”, with the most relevant journals listed in Table 4. Among them, the source with the highest number of publications is AIStech–iron and steel technology conference proceedings—with 66 documents, followed by Applied Sustainability (Switzerland) with 38 documents.
Bradford’s Law is confirmed as valid, since sixteen journals produce 18.09% of articles, while most, 81.91%, come from other journals. Research on a topic will be spread across a limited number of journals, which then attract more articles as the subject expands. This law is relevant in bibliometrics, as it supports academic libraries with literature searches and journal evaluations, and it helps model scientific knowledge production.
The Number of authors according to written documents is selected from the authors section of R Bibliometrix. Lotka’s Law refers to the productivity of authors, revealing that a significant percentage of the literature is produced by a limited number of authors, which identifies the centers of research in a given area. This principle is fundamental to understanding the structure of scientific fields and identifying their key contributors. Of the 1907 documents analyzed, we identified 4058 authors, where we may conclude that Lotka’s Law has been met. It remains a powerful tool for understanding the dynamics of authorship in scientific publishing and is a pillar of quantitative research analysis. Table 5 displays the number of authors according to written documents.
The word cloud is selected from the documents section of R Bibliometrix and highlights the most commonly used terms related to this research. Key terms such as “analytical hierarchy process,” “maintenance,” “decision-making,” and “hierarchical systems” are prominently featured, as shown in Figure 5.
We employ the graph creation resource based on bibliographic information, using co-occurrence analysis among all the keywords. Every keyword is addressed as a node in these networks, with the node’s size reflecting the total number of citations received. The highest nodes represent a greater number of citations, indicating a major influence in the field. Integrating the study, we found 12,580 keywords with a minimum of 5 occurrences, which yielded eleven linked clusters. Once Zipf’s Law calculates the frequency of term occurrences in the literature, it aids in creating a list of the most often used terms in a given research field. This matter satisfies Zipf’s Law.
As seen in Figure 6, the graph was obtained using the full counting method with 814 items: Analytic Hierarchy Process, steelmaking and steel industry, decision-making and AHP, maintenance and hierarchical systems, costs, sustainable development, multi-criteria decision-making, TOPSIS, and maintenance strategies.
According to [74], VOSviewer software features an integrated network visualization function that employs algorithms such as Kamada–Kawai and Fruchterman–Reingold to arrange nodes in a two-dimensional space based on their relationships derived from bibliographic data. VOSviewer uses internal algorithms that work in the background to create network visualizations, rather than relying on command-line tools with explicit commands. The main functions or techniques employed are visualization of similarities, two-dimensional distance-based map, multi-dimensional scaling, and clustering [75].
⮚
Cluster 1 (red) with 183 items, accounting for 23%, highlights 183 main points. The main ones are the Analytic Hierarchy Process, risk assessment, reliability analysis, fuzzy analytic hierarchy, fuzzy set theory, and condition-based maintenance. The discussion included the Analytic Hierarchy Process correlated with risk, reliability, and condition-based maintenance.
⮚
Cluster 2 (green) with 146 items, accounting for 18%, highlights 146 main points. The main ones are the analytical hierarchy process, multicriteria analyses, decision support system, sustainable development, and sustainability. At this point, the debate focuses on Analytic Hierarchy Process and sustainability.
⮚
Cluster 3 (blue) with 131 items, accounting for 16%, highlights 131 main points, the main ones being the steelmaking, iron and steel industry, predictive maintenance, maintenance activity, and condition monitoring. This subject addresses issues related to maintenance and the steel industry.
⮚
Cluster 4 (yellow) with 70 items, accounting for 9%, highlights 70 main points. The main ones are the AHP, the iron and steel industry, risk assessment, accident prevention, and steel. This section emphasizes the AHP method, using hierarchical systems in the steel industry.
⮚
Cluster 5 (purple) with 66 items, accounting for 8%, highlights 66 main points, the main ones being the maintenance, hierarchical systems, maintenance strategies, and sensitivity analysis. This section discusses maintenance within hierarchical systems.
⮚
Cluster 6 (light blue) with 44 items, accounting for 5%, highlights 44 main points, the main ones being costs, cost-effectiveness, cost–benefit analysis, and investments. This theme addresses issues relating to costs.
⮚
Cluster 7 (orange) with 44 items, accounting for 5%, highlights 44 main points, the main ones being multicriteria decision-making and decision support systems. This theme addresses issues relating to MCDM analysis and decision support systems.
⮚
Cluster 8 (brown) with 38 items, accounting for 5%, highlights 38 points. The main ones are multicriteria decision-making and budget control. This theme addresses issues relating to MCDM analysis related to budget control.
⮚
Cluster 9 (pink) with 35 items, accounting for 4%, highlights 35 points, the main ones being life cycle, preventive maintenance, and reliability. This subject addresses issues related to maintenance the life cycle.
⮚
Cluster 10 (salmon) with 32 items, accounting for 4%, highlights 32 points, the main ones being repair, maintainability, and competition. This theme addresses issues relating to repair regarding maintainability.
⮚
Cluster 11 (light green) with 25 items, accounting for 3%, highlights 25 points. The main ones are quality control, AHP, and failure. This section emphasizes the AHP method with quality.
We can see that the red, yellow, and green clusters focus on the analytical hierarchy process, which has the highest frequency of terms. This is followed by the blue cluster, which focuses on the steel industry and maintenance. The word “AHP”—or Analytic Hierarchy Process—is found in almost all the clusters; however, few studies have been published that connect the words “AHP”, “maintenance”, and “steel industry”.
Predictive maintenance is underused in the steel industry, even though an effective maintenance strategy—along with proper management and accurate system monitoring—ensures reliable system operation.
Organizations are increasingly adopting advanced monitoring techniques to tailor their maintenance strategies. Predictive maintenance has emerged as a promising solution for manufacturing industries, where performance is crucial [52].
The R program Bibliometrix provides a report that checks the relevance and development of the subject according to the number of publications and the impact factor H (Impact Index H).
The research themes are categorized into four quadrants as mentioned by [76]:
(a)
Motor themes are key subjects within a field of study that are well-developed and central to ongoing research and advancements in that area. These themes are found in the upper right quadrant.
(b)
Niche themes, located in the upper left quadrant, are specialized and well-developed but have low centrality and strong density. They tend to be somewhat isolated from traditional research and the broader research landscape.
(c)
Emerging or declining themes, located in the lower left quadrant, have low centrality and low density. They signify new research areas or those that are diminishing in importance, with their future relevance depending on advancements in the domain.
(d)
Basic themes located in the lower right quadrant are significant to the research field but remain underdeveloped. They possess potential for future growth and further exploration.
The thematic map selected from the conceptual structure section of Bibliometrix highlights an analysis based on Figure 7, which illustrates the relevance and development degree of various themes. In the upper right quadrant of the “Motor Themes” group, we can see that the topics of Analytic Hierarchy Process, decision-making, and hierarchical systems are thriving. It indicates that this subject remains relevant to the scientific field, providing many fields of study. The terms “steelmaking” and “steel industry” are in the lower right-hand quadrant under “Basic Theme,” but it is closer to the “Motor Themes” quadrant. Thus, we can see that there are opportunities for scientific production in the context of the Analytical Hierarchy Process in the steel industry.
Ref. [77] states that to create a thematic map using the Bibliometrix R package, the main function to utilize is thematicMap(). This function generates a thematic map based on co-word network analysis and clustering [78].

5. Discussion of Results

As per [79], bibliometrics is commonly used to assess scientific output across various fields of study, which allows the measurement of researchers’ impacts on science. It is based on statistical and mathematical tools, making it possible to measure the contributions of researchers to science.
According to [80], bibliometrics refers to the statistical analysis of bibliographic data, which is highly valuable for establishing the historiography of academics, subject areas, academic outlets, institutions, or nations to inform policy design and evaluate performance. Numerous studies in the literature have conducted bibliometric reviews of academic journals. These studies primarily utilized bibliometric indicators, such as citation counts and the h-index, to compare the performance of several journals [81].
There are several publications concerning literature reviews with the application of bibliometric analysis. For examples, [4] employs a bibliometric analysis to assess publication trends in the field of reliability allocation. Ref. [5] presents a comprehensive literature review via bibliometric analysis concerning green grain warehousing. Ref. [6] explores the techniques and emerging trends for equipment maintenance systems by using a bibliometric analysis method. Ref. [12] investigates optimization strategies in water resources management through bibliometric analyses. Finally, Ref. [68] demonstrates a bibliometric analysis to understand green human resource management scholarship using the Bibliometric R-package and VOSviewer software
However, their analyses have been somewhat limited in scope. In contrast, our current paper offers a more in-depth exploration by examining the evolution of applications of the AHP method in maintenance management, specifically within the steel industry, covering the period from 2015 to 2024, where we chose the Scopus database. Therefore, this study presents partial results from an ongoing research project, where the findings discussed in this article are relevant to a continuously evolving investigation.
By observing commonalities and best practices in AHP structuring across numerous studies, bibliometric analysis can contribute to the development of more standardized and robust AHP frameworks for specific decision problems in the steel industry.
AHP was chosen for this research due to its comprehensive and systematic approach to modeling decision problems. It allows for effective representation and quantification of critical variables, ensuring that all stakeholders easily understand the helpfulness of this method in assertive decision-making. Furthermore, as the most commonly applied MCDM method, AHP offers practical advantages. It can be implemented without requiring organizations to acquire specialized software, making it accessible for real-world applications.
Ref. [82] carried out a bibliometric analysis for steel scoria in steel plants, which suggested that this statistical approach can help assess or evaluate development trends in some fields of research over time, making it possible to extract relevant information from a given number of publications. This approach can be used to identify countries, journals, citations, and more.
This study revealed a consistent increase in the number of articles and citations in this subject area, with a growth rate of 77% from 2015 to 2024. Our findings also indicate that the countries producing the most published articles are China, India, Iran, and the United States. Additionally, engineering and computer science are the fields that receive the most focus.
The Journal of AIStech–Iron and Steel Technology Conference Proceedings, proved to be the journal with the highest number of documents published. However, despite this quantity, it has received fewer citations overall. In contrast, the Journal of Cleaner Production has the most citations, which indicates the source with a higher citation impact per document. The author who stands out for relevance and impact is Liu Y., as evidenced by the number of publications and citations, along with a strong academic impact measured by the H-index.
Cluster analysis of the articles has revealed three prominent areas: Analytical Hierarchy Process, the steel industry, and maintenance. While the term “AHP” or “Analytic Hierarchy Process” is found in nearly all the clusters, very few studies have been published that link AHP with maintenance in the context of the steel industry.
Based on Bradford’s Law, we identified 1007 sources, concluding that this law holds true, as only sixteen journals account for 18.09% of the articles, while the remaining 81.91% are distributed among other journals. Regarding Lotka’s Law, we found that 81.8% of the 4058 authors produced only one written document, which means that this law has been met. Additionally, we identified 12,580 keywords that each had a minimum of five occurrences to include in our analysis. This led to the formation of several interconnected clusters that align with Zipf’s Law.
Finally, the thematic map, based on relevance and degree of development, illustrates the Analytic Hierarchy Process, decision-making, and hierarchical systems in the upper right quadrant of the “Motor Themes” group, indicating that this subject remains relevant to the scientific field and offers numerous avenues for study. Through this document, we can see that, with the application of statistical tools related to bibliometric analysis, it is possible to assist researchers in conducting comprehensive literature reviews.
Multicriteria decision-making methods, like AHP, offer potential benefits to organizations in the steel sector; therefore, it is essential to carefully evaluate the selection of a specific method for each unique case.

6. Future Research

To build a future research agenda, we used the statistical software VOSviewer. The type of analysis is related to “co-occurrence”, the counting method is “fractional counting”, and the unit of analysis is “author keywords”. The minimum number of keywords is ten, and we use the “association strength” method. For future studies, we highlight the topics related to the AHP, maintenance, and steel industry, as shown in Figure 8.
(1)
Predictive maintenance: Authors in [54] recommended future research directions aimed at continual improvement, emphasizing advanced analytics, sensor technologies, and applications across various industries. The authors [55] suggest the use of IoT technologies and AI for better maintenance predictions.
(2)
Preventive maintenance: According to [9], the proposed preventive maintenance plan based on AHP can be applied to other assets for establishing better cost–benefit solutions.
(3)
Industry 4.0: As per [55], they recommended the application of advanced techniques for early fault detection, enhancing connectivity, and data collection. According to [9], some tools like artificial intelligence (AI), Big Data, Cloud Computing, Cyber-Physical Systems (CPS), Internet of Things (IoT), Virtual/Augmented Reality (VR/AR) can be used to contribute to maximizing operational efficiency and to achieve operational excellence.
(4)
Machine learning: The authors [55] relate the need for exploring this topic. According to [9], machine learning (ML) can be used to contribute to maximizing operational efficiency and to achieve operational excellence.
(5)
Sustainability: According to [55], the assessment of sustainable performance should be applied to a broader spectrum of the steel industries, and also to the sustainable panorama of organizations becoming a useful tool for stakeholders to analyze business actions and allocate resources and investments. In [60], they refer to applying the AHP method to the assessment of efficiency and sustainability in other industries besides the chemical industry. New criteria for evaluating the sustainability of steel manufacturing organizations are put out by the authors [9].

7. Conclusions

This research employs statistical tools to measure researchers’ scientific contributions concerning the AHP method’s applications in maintenance management within the steel industry. However, the study of AHP in the steel industry extends beyond simply cataloging its applications through bibliometric analysis. It provides a data-driven, overarching perspective that reveals connections, patterns, and trends. These insights have the potential to significantly transform how AHP is applied to the complex and evolving challenges within the steel industry.
To address the research question, it can be confirmed that using VOSviewer and the Bibliometrix package in R allows for the extraction of information relevant to the three bibliometric laws: Lotka’s, Zipf’s, and Bradford’s. Bibliometrix (R) provides effective computational tools for analyzing and verifying bibliometric laws using quantitative data. In contrast, VOSviewer offers engaging visualizations that help in understanding and presenting these principles. When used together, these tools provide a comprehensive approach to bibliometric analysis. This information aids in understanding the research patterns pertinent to the objectives of this study.
Bibliometric analysis, which involves examining keyword co-occurrence and trend analysis, can identify emerging areas where the AHP is increasingly applied within the steel industry. For instance, if terms like “sustainable supply chain,” “decarbonization,” or “Industry 4.0 adoption” are experiencing significant growth in AHP-related publications, this indicates new opportunities for their usage. Such insights can help guide researchers and practitioners in addressing critical and contemporary challenges facing the steel sector. Other suggestions for future studies would cover a comparison of the advantages and limitations of applying the different methods in steel plants or carrying out a bibliographic analysis in other databases and comparing the results obtained since this study focused mainly on the Scopus database. Propose AHP-driven equipment monitoring solutions, or an AHP maintenance workflow (e.g., weight allocation → strategy selection), could be other themes for future studies.

Author Contributions

Conceptualization, N.M.d.M.T., V.A.P.S. and L.E.Q. methodology, N.M.d.M.T., V.A.P.S. and L.E.Q.; software, N.M.d.M.T., V.A.P.S. and L.E.Q.; validation, N.M.d.M.T., V.A.P.S. and L.E.Q.; formal analysis, N.M.d.M.T., V.A.P.S. and L.E.Q.; resources, N.M.d.M.T., V.A.P.S. and L.E.Q.; data curation, N.M.d.M.T., V.A.P.S. and L.E.Q.; writing—original draft preparation, N.M.d.M.T., V.A.P.S. and L.E.Q.; writing—review and editing, N.M.d.M.T. and V.A.P.S.; visualization, N.M.d.M.T., V.A.P.S. and L.E.Q.; supervision, V.A.P.S.; project administration, N.M.d.M.T.; funding acquisition, V.A.P.S. All authors have read and agreed to the published version of the manuscript.

Funding

São Paulo Research Foundation (FAPESP), Grant 2023/14761-5.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

To the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES), through the Graduate Support Program (PROAP), and the Sao Paulo Research Foundation (FAPESP), Grant 2023/14761-5.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Stages of bibliographical research.
Figure 1. Stages of bibliographical research.
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Figure 2. Thematic area–Scopus.
Figure 2. Thematic area–Scopus.
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Figure 3. Country distribution–Scopus.
Figure 3. Country distribution–Scopus.
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Figure 4. Contemporaneity–Scopus.
Figure 4. Contemporaneity–Scopus.
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Figure 5. Word cloud—R Bibliometrix.
Figure 5. Word cloud—R Bibliometrix.
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Figure 6. Network Visualization—VOSviewer.
Figure 6. Network Visualization—VOSviewer.
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Figure 7. Thematic map—R Bibliometrix.
Figure 7. Thematic map—R Bibliometrix.
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Figure 8. Topics associated with AHP for future research—VOSviewer.
Figure 8. Topics associated with AHP for future research—VOSviewer.
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Table 1. Research documents from the Scopus and Web of Science databases.
Table 1. Research documents from the Scopus and Web of Science databases.
Year“AHP” OR “Analytic Hierarchy Process”Maintenance“Steel Industry” OR “Steel Plant”
ScopusWeb of ScienceScopusWeb of ScienceScopusWeb of
Science
20245965333949,97331,1121290683
20235645319347,64830,1471134614
20225578363746,92832,4251121614
20215241336345,41733,0261149513
20204643304141,70130,6721220514
20193970231039,18725,999922380
20183321200935,82624,049856380
20172779194934,15523,400826356
20162571196932,52622,413889351
20152225167530,92421,431729274
Total41,93826,485404,285274,67410,1364679
Table 2. Total scientific documents in the Scopus database.
Table 2. Total scientific documents in the Scopus database.
Year“AHP” OR “Analytic Hierarchy Process” AND
“Maintenance”		“Maintenance”
AND “Steel Industry”
OR “Steel Plant”		“AHP” OR “Analytic Hierarchy Process” AND
“Steel Industry” OR “Steel Plant”
2024204325
2023222278
2022193272
20212074710
2020178539
2019163313
2018114238
2017103216
201687254
201579115
Total155029760
Table 3. Most cited articles by keyword.
Table 3. Most cited articles by keyword.
KeywordsMore CitedReference
“AHP” OR “Analytic Hierarchy Process” AND
“Maintenance”		335An investigation on lean-green implementation practices in Indian SMEs using analytical hierarchy process (AHP) approach
[71]
“Maintenance”
AND “Steel industry” OR “Steel Plant”		147A predictive model for the maintenance of industrial machinery in the context of Industry 4.0
[72]
“AHP” OR “Analytic Hierarchy Process” AND
“Steel industry” OR “Steel Plant”		311Risk evaluation using a novel hybrid method based on FMEA, extended MULTIMOORA, and AHP methods under fuzzy environment
[73]
Table 4. Leading sources—R Bibliometrix.
Table 4. Leading sources—R Bibliometrix.
JournalRankDocuments
AIStech–Iron and Steel Technology Conference Proceedings166
Sustainability (Switzerland)238
AIP Conference Proceedings326
IOP Conference Series: Materials Science and Engineering326
Applied Sciences (Switzerland)421
Journal of Physics: Conference Series520
IOP Conference Series: Earth and Environmental Science617
International Journal of Quality and Reliability Management716
Journal of Cleaner Production716
ACM International Conference Proceeding Series815
Advances in Intelligent Systems and Computing815
Proceedings of the International Conference on Industrial Engineering and Operations Management815
Water (Switzerland)914
Energies (Switzerland)914
Gaoya Dianqi/High Voltage Apparatus1013
Journal of Quality in Maintenance Engineering1013
Table 5. Number of authors according to written documents—R Bibliometrix.
Table 5. Number of authors according to written documents—R Bibliometrix.
Documents WrittenN. of AuthorsProportion of Authors
1405881.8%
24929.9%
31843.7%
4791.6%
5470.9%
More than 51002.0%
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Torre, N.M.d.M.; Salomon, V.A.P.; Quezada, L.E. Statistical Tools Application for Literature Review: A Case on Maintenance Management Decision-Making in the Steel Industry. Stats 2025, 8, 80. https://doi.org/10.3390/stats8030080

AMA Style

Torre NMdM, Salomon VAP, Quezada LE. Statistical Tools Application for Literature Review: A Case on Maintenance Management Decision-Making in the Steel Industry. Stats. 2025; 8(3):80. https://doi.org/10.3390/stats8030080

Chicago/Turabian Style

Torre, Nuno Miguel de Matos, Valerio Antonio Pamplona Salomon, and Luis Ernesto Quezada. 2025. "Statistical Tools Application for Literature Review: A Case on Maintenance Management Decision-Making in the Steel Industry" Stats 8, no. 3: 80. https://doi.org/10.3390/stats8030080

APA Style

Torre, N. M. d. M., Salomon, V. A. P., & Quezada, L. E. (2025). Statistical Tools Application for Literature Review: A Case on Maintenance Management Decision-Making in the Steel Industry. Stats, 8(3), 80. https://doi.org/10.3390/stats8030080

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